A major development in the communications field has been the introduction of Internet Protocol (IP) networks in which packet traffic is routed at a number of network nodes in order to reach its destination.
The technique has the advantage of low cost and the ability to carry a wide range of traffic types and services. A particular problem that has resulted from the introduction of IP networks has been the explosive growth of traffic which has led to congestion. A feature of this IP traffic is that it is inherently ‘bursty’ in nature, i.e. there are rapid variations in bit rate. During the traffic flow peaks, congestion can occur and this in turn has led to packet loss and consequent delays in transmission. For best effort data services, packet loss results in a delay in data transfer which, while not critical to the user, is perceived as a reduction in the capacity of service that is provided. For time critical services, such as voice, the loss of packets can have a deleterious effect on the transmission quality resulting in failure to meet the high quality of service criteria that are demanded from such services.
A particular problem with telecommunications traffic is that it has been found to exhibit an inherently ‘bursty’ nature rather than purely random statistical properties. As a consequence, the traffic that is being transported tends to retain its inherent ‘bursty’ nature even when a number of such traffic streams are aggregated on to a common path. This feature of communications traffic is attributed to long range dependence (LRD) which is a statistical phenomenon related to chaos theory and which, loosely speaking, is associated with time series which are correlated over a number of time-scales. It has long been known that LRD is found in network traffic and causes degradation in network performance. Because LRD traffic is more “bursty” than a typical Poisson distribution model previously used to model telecommunications networks, packet loss is more likely with an LRD traffic stream of the same overall volume. The level of long range dependence (LRD) in a time series is characterised by the Hurst parameter ‘H’ where, 0<H<1. A value of H=0.5 is characteristic of data with no long range dependence, and 0.5<H<1 implies that long range dependence is present, (0<H<0.5 implies anti-long range dependence in which a time series has negative correlation over a number of time scales).
Descriptions of the long-range dependence of communications packet traffic are provided in the following reference documents:    V. Paxson et al ‘Wide Area Traffic: the failure of Poisson Modelling’, IEEE Transaction on Networks, Vol.3, No.3, June 1995, pp226–244.    W. E. Leland et al., ‘On the Self Similar Notice of Ethernet Traffic’, IEEE/ACM Transactions on Networking, Vol.2, No.1, February 1994, pp 1–15.    B. K Ryu et al, ‘The importance of Long-Range Dependence of VBR Video Traffic in ATM Traffic Engineering’, Computer Communication Review, Vol. 26, pp 3–14, October 1996.
The current approach to the problem of the bursty nature and the long range dependence of communications traffic is to over-provision the network switches and routers with buffers to cope with the burstiness of the traffic, i.e. buffers of sufficient capacity to handle traffic flow peaks, and/or to allow the over capacity traffic to be dropped whenever a buffer becomes filled to capacity. Both approaches are less than satisfactory, the first on cost and complexity grounds and the second on the grounds of potential reduction of quality of service. Further, as buffer sizes increase, there is a corresponding increase in the delay in processing the traffic. This can be a significant factor when handling delay sensitive traffic such as voice.
In an attempt to address the congestion problem and to carry greater volumes of traffic more efficiently, networks are being introduced comprising an edge network providing access to an optical core. Within the optical core, traffic is carried between nodes on optical fibre paths which provide a large bandwidth capability for handling significant volumes of traffic. Within the core, routers are relatively simple and perform a rapid switching function. While this technique has the potential to greatly increase the capacity and speed of communications networks, it has introduced the problem of packet queuing at routers within the optical core thus detracting from the high speed nature of the core routers. Further, there is considerable motivation towards the goal of optical packet switching, and this will require the avoidance of packet queuing at core routers. For this reason, many workers have been developing techniques for controlling traffic in the edge network so that traffic is routed into the core only if sufficient resources are available within the core to handle that traffic. Where congestion is occurring in the edge network, this can then lead to the rejection of requests for service, a reduction in the quality of service perceived by the customer, and a potential loss of revenue to the network operator.
In order to reduce the effects of congestion and thus handle a greater volume of traffic, various workers have proposed controlled scheduling of the traffic queues in a manner that smoothes the peaks on bursts of traffic so that the core network can then process a more uniform traffic flow. However, in order to achieve this controlled scheduling in an optimum manner, knowledge of the statistical properties of the traffic flow is required. As discussed above, it has been found that packet traffic in a network does not have a smooth random pattern but instead displays a long range dependency. It is necessary to have a measure of this long range dependency before an efficient process of queue scheduling and traffic congestion can be determined. It will also be understood that this long-range dependency is not constant but varies with changes in the traffic mix and content.
As discussed above, it is generally accepted that the degree or magnitude of long-range dependence of communications packet traffic is characterised by the statistical measure known as the Hurst parameter. In theory, a knowledge of the Hurst parameter would then permit appropriate scheduling and aggregation of traffic to provide a substantially uniform flow in the core network. However, calculation of the Hurst parameter is a complex operation requiring significant computer power. It is impractical to perform this calculation at a reasonable cost and with sufficient rapidity to provide real time information for the processing of communication traffic.